Emotional Speech Classification Based on Multi View Characterization

Emotional speech classification is a key problem in social interaction analysis. Traditional emotional speech classification methods are completely supervised and require large amounts of labeled data. In addition, various feature sets are usually used to characterize the emotional speech signals. Therefore, we propose a new co-training algorithm based on multi-view features. More specifically, we adopt different features for the characterization of speech signals to form different views for classification, so as to extract as much discriminative information as possible. We then use the co-training algorithm to classify emotional speech with only few annotations. In this article, a dynamic weighted co-training algorithm is developed to combine different features (views) to predict the common class variable. Experiments prove the validity and effectiveness of this method compared to self-training algorithm.